Recent advances in flow cytometry (FACS) technology offer new and exciting approaches to understanding, monitoring and combating HIV disease. Multiparameter FACS (4 color, 2 scatter) instruments that measure four to six fluorescence colors are already routinely used by forefront laboratories doing HIV vaccine development and key HIV research. High-dimensional FACS instruments now coming into this arena extend this capability by providing high-quality measurements for up to a dozen or more fluorescence colors on or in individual cells. In addition, staining methods that detect memory and other T cell surface markers in combination with intracellular cytokines, transcription factors, phosphoproteins and virally-derived proteins now enable measurement of functional responses in ever more finely divided T cell subsets; and, new data analysis methods now enable better visualization of Hi-D FACS data and extraction of findings from that data. Much of this technology is already being applied in laboratories developing new assays to monitor HIV vaccine effectiveness. However, broad utilization of the new (and the older) FACS technology is sharply limited both by the time and effort required to learn how to effectively use the technology and, importantly, by the as yet unsolved problem of how to routinely apply the technology to assaying the large numbers of samples encountered in testing candidate HIV vaccines and other kinds of HIV clinical research. Studies proposed here address these issues, which are particularly crucial to progress in the development of HIV vaccines. We have already developed knowledge-based protocol design tools that speed up and the development and implementation of FACS staining protocols, facilitate FACS data collection, and permanently store protocol information necessary for FACS data analysis. In addition, we have already designed methods/software that can operate without intervention to standardize Hi-D FACS data and correct (compensate) for fluorescence overlaps. Finally, we have already implemented prototypes of new clustering algorithms that are statistically appropriate for analyzing FACS data, i.e., unlike microarray and other clustering algorithms, our algorithms work for data sets that contain relatively few measurements (fluorescence colors, etc.) taken for a very large number of items (cells or other particles). Here, we propose to complete and link these software components to create a working prototype that will support HIV vaccine development by developing software that will 1) automate preparation of FACS data for analysis (data standardization and fluorescence compensation); 2) facilitate FACS data analysis ("clustering" to provide subset identification and gating); 3) automate of statistical comparisons of data from test and reference samples in clinical vaccine trials; and thereby, 4) facilitate and improve the ability to evaluate HIV vaccine responses in research and clinical studies. [unreadable] [unreadable] [unreadable]